Richard Oentaryo
Head Of AI ML Solutions, Retail Product, Account & Txn Pillar, CBG Retail Data Chapter
Singapore, Singapore
Summary
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Rockstar🎓
Top SchoolRichard Oentaryo is a Head of AI/ML Solutions based in Singapore with over two decades of experience translating advanced research into large-scale commercial impact, currently overseeing a regional portfolio of 250+ models that generate nine-figure annual value for DBS. He blends deep technical pedigree—a PhD in Computer Engineering and 40+ peer-reviewed publications—with hands-on engineering, having improved causal inference tooling and GPU support in notable open-source projects like McKinsey’s causalnex. At DBS he has driven Agentic AI, Quantum AI and Federated/Causal AI experiments while shaping strategic product roadmaps for remittances, loans and cards across four markets. Previously he led data-science transformation engagements at McKinsey and operationalized F1-grade telemetry analytics at McLaren Applied, consistently turning prototypes into reusable enterprise assets. Known as a coach and investor in the data community, he bridges academic rigor and product leadership to scale trustworthy AI in regulated industries. Less obvious: he combines deep algorithmic invention with pragmatic productionization—often shipping research ideas as bank-grade, reusable platforms.
9 years of coding experience
6 years of employment as a software developer
Doctor of Philosophy, Computer Engineering, Doctor of Philosophy, Computer Engineering at Nanyang Technological University Singapore
English, Indonesian
Stackoverflow
251reputation
9kreached
0answers
1question
Github Skills (18)
pytorch10
python10
data-science10
machine-learning10
causal-inference10
bayesian-network10
pandas9
unit-testing9
causal8
bayesian-inference8
bayesian8
causality8
github-ci7
githubaction-workflow7
numba6
Programming languages (1)
Github contributions (5)
mckinsey/causalnex
Jul 2021 - Aug 2022
A Python library that helps data scientists to infer causation rather than observing correlation.
Role in this project:
Data Scientist & ML Engineer
Data Scientist & ML EngineerContributions:25 reviews, 25 commits, 11 PRs in 1 year 1 month
Contributions summary:Richard primarily focused on improving the causal inference capabilities of the `causalnex` library. Their contributions included fixing unit tests, addressing linting issues, and refactoring the code. They also added support for GPU usage in the PyTorch-based NOTEARS implementation and implemented the Expectation-Maximization (EM) algorithm for learning with latent variables. Furthermore, they provided examples for graph exporting.
pythoncausal-inferencecausal-modelscausal-networksbayesian-networks
oentaryorj/smu.softeng.crossact
Jan 2019 - Jul 2021
Cross-platform activity prediction
Contributions:4 commits, 2 PRs, 14 pushes in 2 years 6 months
predictiondeep-learningembeddingsmulti-task-learningmachine-learning
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